DocumentCode :
2550639
Title :
Collaboratively mining sequential patterns over private data
Author :
Zhan, Justin
fYear :
2007
fDate :
7-10 Oct. 2007
Firstpage :
3323
Lastpage :
3326
Abstract :
To conduct data mining, we often need to collect data from various parties. Privacy concerns may prevent the parties from directly sharing the data. A challenging problem is how multiple parties collaboratively conduct data mining without breaching data privacy. The goal of this paper is to provide solutions for privacy-preserving sequential pattern mining for horizontal collaboration. Our goal is to obtain accurate mining results without disclosing private data.
Keywords :
data mining; data privacy; groupware; collaborative mining; data mining; data privacy; sequential pattern mining; Collaboration; Data mining; Data privacy; Itemsets; Marketing and sales; Sorting; Transaction databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
978-1-4244-0990-7
Electronic_ISBN :
978-1-4244-0991-4
Type :
conf
DOI :
10.1109/ICSMC.2007.4414221
Filename :
4414221
Link To Document :
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